Navigating the new normal: Advertising strategies in the post-ATT world

Apple's App Tracking Transparency (ATT) policy, introduced in iOS 14, has had a significant impact on the mobile marketing landscape. It requires apps to ask users for permission before tracking their data for advertising purposes, and has led to many users opting out of tracking.

The effects of ATT have been felt across the mobile marketing ecosystem. Under ATT, marketers face two main challenges: visibility loss and signal loss. In order to effectively tackle these challenges, it is important for marketers to approach each challenge separately and develop strategies that specifically address visibility loss and signal loss. In this article, we will explore some of the strategies and technologies that marketers have used to cope with visibility loss and signal loss.

Coping with the visibility loss

Visibility loss refers to the difficulties that marketers may have in measuring the effectiveness of their advertising channels, campaigns, and/or creatives, as the limitations imposed by ATT reduce the amount of data that can be collected. Here are some strategies to think about:

1. Use SKAdNetwork.

SKAdNetwork (SKAN) is a framework introduced by Apple that provides a way for advertisers to measure the effectiveness of their campaigns without collecting device-level or user-level data.

2. Set up and monitor metrics **in a way that is more friendly to SKAN.**‍

Here are some tips to consider:

• Use ad platform metrics such as click-through rate (CTR) and cost per mille (CPM), which are more reliable and responsive. Monitor these metrics regularly to not only gain insight at the creative level, but also make quick performance assessments.
• Utilize SKAN-based installs and associated metrics such as conversion rate (CVR), cost per install (CPI), and install per mille (IPM), as SKAN-based install counts are more consistent.
• Consolidate marketing channels and campaigns and focus budget on select channels and campaigns to ensure meeting the privacy threshold. Under SKAN 3.0, 10-20 installs within the last 24 hours is considered the empirical privacy threshold. However, keep in mind that the recommended number of installs per campaign within the last 24 hours can vary by advertising platform, and the recommended number of campaigns per advertising channel can also differ.
• Set up SKAN schema to capture early conversions, as there is a SKAN postback delay. Specifically, you can set up SKAN conversion values to capture D0 metrics. Since most direct response campaigns show cumulative conversions or revenue skewed towards Day Zero or D0, capturing D0 metrics will not only allow you to act quickly, but also give you sufficient visibility to optimize your campaigns. Also, this tip will be particularly useful for SKAN 4.0 users, as SKAN 4.0 introduced the lock feature, which will allow advertisers to close the conversion window and immediately send the nth SKAN postback.
• Use the insights gained from Android and iOS interchangeably, as over-performing and under-performing creatives may be consistent across both platforms.
• Avoid over-optimization. Each ad platform has its own optimization and automated budget reallocation algorithms. Due to SKAN delays, you should expect campaign optimization algorithms to also react relatively slower. Therefore, give campaigns more time to learn and adapt and avoid too much agile reallocation of your budget.
• Lastly, understand modeled conversions on each ad platform such as Google and Meta and use them to gain supplementary information. Modeled conversions are designed to fill in the visibility gap under SKAN 3.0 and 4.0, such as unavailable creative-level analysis or missing post-install information due to the privacy threshold. Carefully read each advertising channel's modeled conversion documentation to understand how it works and use it for supplementary information in addition to metrics from mobile measurement partners (MMPs).

3. Consider using marketing mix modeling.

Marketing mix modeling, or MMM, is a method of attribution that uses statistical modeling to estimate the relative effectiveness of different advertising channels based on aggregated data, even in the absence of device-level or user-level data.

4. Try out lift studies.

Lift studies are experiments that measure the effectiveness of advertising campaigns by comparing the performance of a control group (who are held out by random selection to be not exposed to the ads) with that of a treatment group (who are exposed to the ads). This allows advertisers to see the incremental lift in conversions or other metrics that can be attributed to their ads.

5. Don't forget about Apple Search Ads.

Keep in mind that Apple Search Ads is an ATT-friendly advertising channel. It uses a separate API called AdServices API, which provides device-level attribution information. Therefore, SKAN is not necessary for tracking attribution on Apple Search Ads.

%%CTA|Try It Free|https://app.airbridge.io/signin?from=blog-inline|See how Airbridge recovers attribution signals lost to ATT.%%

Coping with the signal loss

Signal loss, on the other hand, refers to the decreased integrity of the user behavioral data that is sent to advertising channels, which can compromise the accuracy of ad targeting. Here are some ideas to ponder:

1. Comply with ATT and collect as many advertising IDs as possible by following Apple’s requirements and asking for users' permission to track their data. This can help maintain the accuracy of ad targeting and measurement.
2. Use additional identifiers such as hashed emails or hashed phone numbers in conjunction with Conversion API (CAPI), which allows ad servers to fetch data directly from the advertiser's server and potentially augment attributes or data that the advertiser has the appropriate rights and permissions to share, but may not have been immediately available through pixel or SDK implementations. These additional identifiers such as hashed emails or click IDs can help to increase the matching rate between advertisers and ad platforms.
3. Use re-engagement signal solutions. These methods append identifiers aggregated at the campaign or other aggregated level to the deep link URIs, allowing ad platforms to collect user behavior data after users enter the app through deep linking. Ad platforms can assess re-engagement performance of campaigns or keywords and reallocate budget or deliver ads to the most successful combinations.

Rethink strategies and get yourself in the lifeboat

Apple’s ATT is a necessary attempt to shift away from indiscriminate collection of user data, giving users more control over their privacy. However, it does make marketers’ life more difficult as they have to rely on users granting permission for tracking.

In this time of compelling changes, marketers can use various tactics and technologies. However, it should be kept in mind that in the end, the key is to approach each issue separately and develop strategies that address the specific challenges of visibility loss and signal loss.

Airbridge is equipped with SKAN and ATT solutions as well as MMM solutions that can put you on a fast track to mobile growth while adhering to Apple’s policies. If you want to talk about your post-ATT concerns, feel free to reach out to our marketing experts!